Image recognition technology is becoming widely used in manufacturing, commercial, and business applications. Such applications include: verification of objects on assembly lines, locating bonding pads on integrated circuits, visual robotic control systems, optical character recognition, and document verification and classification devices.
Typical image recognition systems known in the art receive data through a data communications link and transmit the data to various recognition boards for processing by a host computer. Typically, in optical character recognition systems, image data files are captured and compressed by image capture platforms and devices. The image data files are then sent to a recognition system to decompress the data and perform image recognition within a pre-defined subset or window of the image.
Image data recognition is a computation intensive task and presents a formidable challenge to the host computer. The limited success of prior art systems that use general purpose computers to perform pattern analysis and image recognition is due to the extremely long processing times required to process images with a large number of data points. The massive amount of data to be analyzed and processed cause many computer systems to be slow and incapable of meeting the data throughput requirements. These complex real time processing demands call for computational throughputs that exceed those of devices used in the prior art.
There is a need for a high speed image preprocessing system which can off-load various tasks from the host recognition system so that a higher system recognition throughput is achieved.